This is a proposal to examine chronic medical problems and life events as risk factors in depressive symptomatology in rural areas through a 5 year followup of 713 rural Tennessee residents interviewed in an earlier USDA-funded study (1976-1980). This study found that chronic medical problems were the most powerful predictor of depression. Further, internal and external resources (individual resources and social support) operated as moderating factors between the stress (of medical problems/life events) and psychiatric impairment. The objective of the followup study is to examine these variables as risk factors associated with depression over time. The conceptual model involves alternative factors of biogenic (chronic medical problems) and sociogenic factors (normative life events) with internal and external resources mediating their impact on depressive symptoms. If the number of biogenic and sociogenic stressors is high at both times, then individuals experiencing them should manifest high levels of symptomatology at followup. Conversely, those experiencing low levels at both times should manifest low symptomatology at followup. From an etiologic standpoint, those individuals experiencing either an increase or decrease in stress over the t1 - t2 interval are significant. The survey instrument (based upon the original questionnaire) will include demographic characteristics, chronic medical problems, normative life events, internal and external resources, and measures of psychiatric impairment (CES-D, GWB, and HOS). The Diagnostic Interview Schedule will be added. Analyses will include cross-lagged panel correlation or panel regression techniques to evaluate the relationships between medical problems/life events and depressive symptomatology over the 5 year interval. Such techniques allow examination of the effects of t1 stress upon t1 and t2 symptoms and, as well, the independence of t1 and t2 stress and possible contamination effects can be assessed. In view of the social drift hypothesis, the proposed study will make extensive efforts to relocate those who have moved since t1. Where repeated attempts to locate respondents fail, attributes of the missing respondents will be compared with those of the remainder of the sample in order to assess possible attrition biases.